Randomize the Future: Asymptotically Optimal Locally Private Frequency Estimation Protocol for Longitudinal Data
Summary: Introduces an online LDP frequency-estimation protocol for longitudinal binary data with error O((1/ε)·log d·√(k n log(d/β))), removing prior linear-in-k dependence and matching the lower bound up to log factors. Key novelty: FutureRand, a randomizer that correlates noise across nonzeros and leverages input-space symmetry precomputation to produce on-the-fly outputs without future knowledge, closing the online/offline error gap. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Olga Ohrimenko
- 2. Anthony Wirth
- 3. Hao Wu
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,909 | Continual Release of Differentially Private Synthetic Data from Longitudinal Data Collections | 2024 | PODS | 4.1945683e-05 |
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